Maximal dispersion of adaptive random walks
Statistical Mechanics
2023-01-24 v2 Disordered Systems and Neural Networks
Abstract
Maximum entropy random walks (MERWs) are maximally dispersing and play a key role in optimizing information spreading in various contexts. However, building MERWs comes at the cost of knowing beforehand the global structure of the network, a requirement that makes them totally inadequate in real case scenarios. Here, we propose an adaptive random walk (ARW), which instead maximizes dispersion by updating its transition rule on the local information collected while exploring the network. We show how to derive ARW via a large-deviation representation of MERW and study its dynamics on synthetic and real world networks.
Keywords
Cite
@article{arxiv.2202.13923,
title = {Maximal dispersion of adaptive random walks},
author = {Gabriele Di Bona and Leonardo Di Gaetano and Vito Latora and Francesco Coghi},
journal= {arXiv preprint arXiv:2202.13923},
year = {2023}
}
Comments
20 pages, 12 figures, 1 table